Specific Aims. In the past 30 years, scientists have learned a great deal from studying genes whose products contribute to differences in aging and lifespan (JOHNSON 2013; VIJG and SUH 2005). Yet, ample evidence shows that the rate of aging is also affected by non-genetic factors (KIRKWOOD and FINCH 2000; MARTIN 2009). For example, genetically identical C. elegans cultured in controlled homogeneous environments eventually reach a point in which some individuals are still capable of normal movement while others are not (HERNDON et al. 2002). There can be a tenfold difference in lifespan between the shortest and longest lived genetically identical C. elegans cultured on the same Petri dish (data from (JOHNSON 1990), analyzed by (KIRKWOOD and FINCH 2002)). The coefficient of variation for lifespan (CV; standard deviation /mean; an appropriate statistic to compare proportional variation) derived from 50 well controlled wild-type C. elegans experiments (homogeneous laboratory environment, Kaeberlein lab) and 1000 pairs of Danish monozygotic twins (heterogeneous environment, (HERSKIND et al. 1996)), gave values of 21% for worms and 23% for humans (Mendenhall et al. unpublished). These facts suggest that non-genetic, stochastic factors (likely including epigenomic changes to chromatin in the adult soma (FRAGA et al. 2005)) contribute significantly to differences in aging rate and lifespan (reviewed in(KIRKWOOD and FINCH 2000)). In 2005, Tom Johnson and coworkers identified a predictor of heterogeneity in lifespan, and I continued these studies in his lab. In genetically identical young adult animals in homogeneous environments, expression of GFP under control of the small heatshock protein promoter, hsp-16.2, defined a variable, whose value predicted subsequent lifespan (MENDENHALL et al. 2012; REA et al. 2005). High expression values thus defined a physiological state that persisted throughout the life of the animal, a state whose consequences included lengthened lifespan, increased resistance to subsequent heat shock, and a lower percentage of life spent immobilized (CYPSER et al. 2013; MENDENHALL et al. 2012; REA et al. 2005). Restated, this long-lived physiological state was defined, operationally, by high expression of a particular reporter gene. Similarly, in yeast, Dr. Brent's lab (COLMAN-LERNER et al. 2005) identified causes of differences in expression of particular combinations of reporters in genetically identical cells cultured in homogeneous environments. These experiments revealed persistent cell-to-cell differences in general ability to express genes into proteins, and in strength of signal transmission through a particular cell signaling pathway. Thus they also operationally defined hitherto unidentified physiological states. In preliminary work, I have extended my research from Colorado to develop rigorous quantitative methods to quantify, in single cells in tissues of C. elegans, previously identified physiological states, and cell- to-ell and animal-to-animal variation in these states. Over the next five years, I will develop numerous single- copy reporter genes to report on other variables, and make use of additional measurements to cast a wide net for additional physiological states. I will determine which physiological states and cellular processes contribute to differences in long term outcomes including rate of aging and lifespan. The central hypothesis of this proposal is that in biologica systems, variation in processes that are temporally upstream causes variation in downstream system outputs. Thus, these experiments will identify key processes (for examples, differences in the activity of particular signaling pathways during early development, or differences in young adult ability to express genes into proteins) for which variation in these measured processes contributes to distinct long term outcomes, and will shed light on the order in which these occur. They will generate data that will address current theories about aging (including, for example, the free radical theory and the disposable soma theory) and produce and test novel hypotheses about mechanisms that result in cell-to-cell and animal-to-animal differences in the rate of aging and lifespan. Finally, these experiments will identify additional reporter gene biomarkers in C. elegans that can be tested for predictive power in other organisms. During the five years of this project I will: Aim 1 (K99): Continue to develop single-copy reporters (>50) and rigorous reporter quantification methods to allow precise measurement of lifespan reporter biomarkers, in order to cast a wide net to quantify distinct physiological states and cellular processes. Aim 2 (K99): Evaluate preexisting and Aim 1-generated transgenic reporter animals to find which cellular reporter levels, signaling events, cellular processes, and organismic parameters are most variable at different points in the life of the animal, in order to decide on restricted subsets of variables to measure longitudinally in Aim 3. Aim 3 (K99/R00): Quantify the Aim 2 and literature-determined parameters longitudinally, from the E cell of the eight cell embryo all the way to the morbid intestine cells of the elderly hermaphrodite, to generate and test hypotheses on causality of inter-individual and inter-cellular variation in gene expression, lifespan and physiological state, to order reported aging pathologies, to establish which theories of aging are most supported by the new data, and to identify additional biomarkers of aging.